ML Sales K-means Clustering

September 23, 2023 (1y ago)

0 views

Data K-means ML Repository Overview

Introduction

This (repository) focuses on implementing K-means clustering to increase sales. It contains Jupyter notebooks and Python scripts that demonstrate the application of machine learning techniques for sales optimization.

Files and Their Roles

1. Increase Sales.ipynb

  • Purpose: Python script that contains the code for increasing sales.
  • Key Features: Data manipulation, feature engineering, and model training, Data exploration, visualization, and preliminary analysis.

2. Kmeans-ML-Sales.ipynb

  • Purpose: A Jupyter notebook that focuses on applying K-means clustering for sales optimization.
  • Key Features: Data preprocessing, K-means clustering, and evaluation Model definition, training, and evaluation metrics.

Workflow

  1. Data Exploration: Start by exploring the data in "Increase Sales.ipynb".
  2. Data Manipulation: Use "Increased-sales.py" for data cleaning and feature engineering.
  3. Model Training: Train the K-means model using "Kmeans-ML-Sales.ipynb" and "kmeans-model.py".
  4. Evaluation: Evaluate the model's performance and interpret the results.

Technologies Used

  • Python
  • Jupyter Notebook
  • K-means Clustering Algorithm

Conclusion

The repository provides a comprehensive guide to applying K-means clustering for sales optimization. It includes all the necessary code and notebooks to understand the process from data exploration to model evaluation.